def test_psi():
"""Test Phase Slope Index (PSI) estimation"""
psi, freqs, times, n_epochs, n_tapers = phase_slope_index(data,
mode='fourier',
sfreq=sfreq)
assert_true(psi[1, 0, 0] < 0)
assert_true(psi[2, 0, 0] > 0)
indices = (np.array([0]), np.array([1]))
psi_2, freqs, times, n_epochs, n_tapers = phase_slope_index(
data, mode='fourier', sfreq=sfreq, indices=indices)
# the measure is symmetric (sign flip)
assert_array_almost_equal(psi_2[0, 0], -psi[1, 0, 0])
cwt_freqs = np.arange(5., 20, 0.5)
psi_cwt, freqs, times, n_epochs, n_tapers = phase_slope_index(
data,
mode='cwt_morlet',
sfreq=sfreq,
cwt_frequencies=cwt_freqs,
indices=indices)
assert_true(np.all(psi_cwt > 0))
assert_true(psi_cwt.shape[-1] == n_times)
def test_psi():
"""Test Phase Slope Index (PSI) estimation"""
sfreq = 50.
n_signals = 3
n_epochs = 10
n_times = 500
rng = np.random.RandomState(42)
data = rng.randn(n_epochs, n_signals, n_times)
# simulate time shifts
for i in range(n_epochs):
data[i, 1, 10:] = data[i, 0, :-10] # signal 0 is ahead
data[i, 2, :-10] = data[i, 0, 10:] # signal 2 is ahead
psi, freqs, times, n_epochs, n_tapers = phase_slope_index(data,
mode='fourier', sfreq=sfreq)
assert_true(psi[1, 0, 0] < 0)
assert_true(psi[2, 0, 0] > 0)
indices = (np.array([0]), np.array([1]))
psi_2, freqs, times, n_epochs, n_tapers = phase_slope_index(data,
mode='fourier', sfreq=sfreq, indices=indices)
# the measure is symmetric (sign flip)
assert_array_almost_equal(psi_2[0, 0], -psi[1, 0, 0])
cwt_freqs = np.arange(5., 20, 0.5)
psi_cwt, freqs, times, n_epochs, n_tapers = phase_slope_index(data,
mode='cwt_morlet', sfreq=sfreq, cwt_frequencies=cwt_freqs,
indices=indices)
assert_true(np.all(psi_cwt > 0))
assert_true(psi_cwt.shape[-1] == n_times)
def test_psi():
"""Test Phase Slope Index (PSI) estimation"""
psi, freqs, times, n_epochs, n_tapers = phase_slope_index(data, mode="fourier", sfreq=sfreq)
assert_true(psi[1, 0, 0] < 0)
assert_true(psi[2, 0, 0] > 0)
indices = (np.array([0]), np.array([1]))
psi_2, freqs, times, n_epochs, n_tapers = phase_slope_index(data, mode="fourier", sfreq=sfreq, indices=indices)
# the measure is symmetric (sign flip)
assert_array_almost_equal(psi_2[0, 0], -psi[1, 0, 0])
cwt_freqs = np.arange(5.0, 20, 0.5)
psi_cwt, freqs, times, n_epochs, n_tapers = phase_slope_index(
data, mode="cwt_morlet", sfreq=sfreq, cwt_frequencies=cwt_freqs, indices=indices
)
assert_true(np.all(psi_cwt > 0))
assert_true(psi_cwt.shape[-1] == n_times)
def get_psi_feats(df, sfreq=125., band='alpha'):
electrodes = df.columns
df = df.copy()
alpha_filter = get_filter(sfreq=sfreq, band=band)
df = df[electrodes]
for el in electrodes:
df[el] = np.convolve(alpha_filter, df[el], 'same')
vals = df.values
vals = vals.transpose(1, 0)
vals = vals[None, :, :]
psi, freqs, times, n_epochs, _ = phase_slope_index(vals,
sfreq=sfreq,
verbose=False)
d = {}
for i in range(psi.shape[0]):
for j in range(i):
d[get_col_name('psi', band, electrodes[i],
electrodes[j])] = psi[i, j, 0]
return d
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices = [src[i]['vertno'] for i in range(2)]
n_signals_tot = 1 + len(vertices[0]) + len(vertices[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
# Compute the PSI in the frequency range 8Hz..30Hz. We exclude the baseline
# period from the connectivity estimation
fmin = 8.
fmax = 30.
tmin_con = 0.
sfreq = raw.info['sfreq'] # the sampling frequency
psi, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts, mode='multitaper', indices=indices, sfreq=sfreq,
fmin=fmin, fmax=fmax, tmin=tmin_con)
# Generate a SourceEstimate with the PSI. This is simple since we used a single
# seed (inspect the indices variable to see how the PSI scores are arranged in
# the output)
psi_stc = mne.SourceEstimate(psi, vertices=vertices, tmin=0, tstep=1,
subject='sample')
# Now we can visualize the PSI using the plot method. We use a custom colormap
# to show signed values
v_max = np.max(np.abs(psi))
brain = psi_stc.plot(surface='inflated', hemi='lh',
time_label='Phase Slope Index (PSI)',
subjects_dir=subjects_dir,
clim=dict(kind='percent', pos_lims=(95, 97.5, 100)))
subjects_dir=subjects_dir)
seed_ts_slow_mt = mne.extract_label_time_course(stcs_slow,
vertex_mt,
src,
mode='mean_flip',
verbose='error')
# Combine the seed time course with the source estimates.
comb_ts_slow = list(zip(seed_ts_slow_v1, seed_ts_slow_mt))
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices = [src[i]['vertno'] for i in range(2)]
indices = (np.array([0]), np.array([1]))
# Compute the PSI in the frequency range 11Hz-17Hz.
fmin = 11.
fmax = 17.
sfreq = slow_epo_isi.info['sfreq'] # the sampling frequency
psi_slow, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts_slow,
mode='fourier',
sfreq=sfreq,
indices=indices,
fmin=fmin,
fmax=fmax)
psi_slow_v1_mt[vert_num_mt] = np.array(psi_slow[0][0])
psi_slow_v1_mt_all[vert_num_v1] = psi_slow_v1_mt
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices = [src[i]['vertno'] for i in range(2)]
n_signals_tot = 1 + len(vertices[0]) + len(vertices[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
# Compute the PSI in the frequency range 10Hz-20Hz. We exclude the baseline
# period from the connectivity estimation.
fmin = 10.
fmax = 20.
tmin_con = 0.
sfreq = epochs.info['sfreq'] # the sampling frequency
psi, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts, mode='multitaper', indices=indices, sfreq=sfreq,
fmin=fmin, fmax=fmax, tmin=tmin_con)
# Generate a SourceEstimate with the PSI. This is simple since we used a single
# seed (inspect the indices variable to see how the PSI scores are arranged in
# the output)
psi_stc = mne.SourceEstimate(psi, vertices=vertices, tmin=0, tstep=1,
subject='sample')
# Now we can visualize the PSI using the :meth:`~mne.SourceEstimate.plot`
# method. We use a custom colormap to show signed values
v_max = np.max(np.abs(psi))
brain = psi_stc.plot(surface='inflated', hemi='lh',
time_label='Phase Slope Index (PSI)',
subjects_dir=subjects_dir,
clim=dict(kind='percent', pos_lims=(95, 97.5, 100)))
idx_v1 = np.searchsorted(stcs[0].vertices[1], stcs_v1[0].vertices[1])
idx_mt = np.searchsorted(stcs[0].vertices[1], stcs_mt[0].vertices[1])
# Construct indices to estimate connectivity between the label time course
indices = seed_target_indices([idx_v1], [idx_mt])
# Compute the WPLI2_debiased in the frequency range
fmin = 8.
fmax = 15.
sfreq = fast_epo_isi.info['sfreq'] # the sampling frequency
# Compute the PSI in the frequency range 8Hz-15Hz.
psi, freqs, times, n_epochs, _ = phase_slope_index(stcs,
mode='fourier',
sfreq=sfreq,
indices=indices,
fmin=fmin,
fmax=fmax,
n_jobs=1)
# Average over values between all vertices [v1_vertices * mt_vertices]
psi_avg = psi.mean(0)
all_v1_mt.append(psi_avg)
stop = timeit.default_timer()
time = stop - start
# Save
np.save(savepath + 'psi/' + 'all_v1_mt_vertices_fast_fastepo', all_v1_mt)
all_subj = np.load(savepath + 'psi/' + 'all_v1_mt_vertices_fast_fastepo.npy')
plt.scatter(np.arange(42), all_subj[:, 0])
plt.plot(np.arange(42), np.zeros(42), 'r--')
plt.title('PSI between V1 and MT for all subjects')
vertices = [np.arange(10242), np.arange(10242)]
n_signals_tot = 1 + len(vertices[0]) + len(vertices[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
fmin = 8.
fmax = 12.
tmin_con = 0.
sfreq = C.sfreq # the sampling frequency
psi, freqs, times, n_epochs, n_tapers = phase_slope_index(
comb_ts_sd,
indices=indices,
sfreq=sfreq,
mode='fourier',
fmin=fmin,
fmax=fmax,
tmin=tmin,
tmax=tmax,
n_jobs=6)
psi_stc = mne.SourceEstimate(psi,
vertices=vertices,
tmin=0,
tstep=1,
subject=sub_to)
# Now we can visualize the PSI using the :meth:`~mne.SourceEstimate.plot`
# method. We use a custom colormap to show signed values
v_max = np.max(np.abs(psi))
brain = psi_stc.plot(surface='inflated',
seed_ts_sd = mne.extract_label_time_course(stc_SD, morphed_labels[k], \
src_SD, mode='mean_flip',return_generator=False)
seed_ts_ld = mne.extract_label_time_course(stc_LD, morphed_labels[k], \
src_LD, mode='mean_flip',return_generator=False)
for f in np.arange(0,len(C.con_freq_band_psi)-1):
f_min=C.con_freq_band_psi[f]
f_max=C.con_freq_band_psi[f+1]
print('Participant : ' , i, '/ ROI: ',k,' win:', win,\
' freq: ',f)
comb_ts_sd = zip(seed_ts_sd, stc_SD)
comb_ts_ld = zip(seed_ts_ld, stc_LD)
psi_SD, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts_sd, mode='fourier', indices=indices, sfreq=500,
fmin=f_min, fmax=f_max, n_jobs=15)
psi_LD, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts_ld, mode='fourier', indices=indices, sfreq=500,
fmin=f_min, fmax=f_max, n_jobs=15)
psi_stc_SD = mne.SourceEstimate(psi_SD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
psi_stc_LD = mne.SourceEstimate(psi_LD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
stc_total_SD[i][k][f][win]= morph_SD.apply(psi_stc_SD)
def SN_effective_connectivity_bands(i, method):
n_subjects = len(subjects)
meg = subjects[i]
sub_to = MRI_sub[i][1:15]
print('Participant : ', i)
stc_SD_file_name = os.path.expanduser(
'~'
) + '/my_semnet/json_files/connectivity/stc_' + method + '200_bands_SD_sub' + str(
i) + '.json'
stc_LD_file_name = os.path.expanduser(
'~'
) + '/my_semnet/json_files/connectivity/stc_' + method + '200_bands_LD_sub' + str(
i) + '.json'
morphed_labels = mne.morph_labels(SN_ROI,subject_to=data_path+sub_to,\
subject_from='fsaverage',subjects_dir=data_path)
# Reading epochs
epo_name_SD = data_path + meg + 'block_SD_words_epochs-epo.fif'
epo_name_LD = data_path + meg + 'block_LD_words_epochs-epo.fif'
epochs_sd = mne.read_epochs(epo_name_SD, preload=True)
epochs_ld = mne.read_epochs(epo_name_LD, preload=True)
epochs_SD = epochs_sd['words'].copy().resample(500)
epochs_LD = epochs_ld['words'].copy().resample(500)
# Equalize trial counts to eliminate bias (which would otherwise be
# introduced by the abs() performed below)
equalize_epoch_counts([epochs_SD, epochs_LD])
# Reading inverse operator
inv_fname_SD = data_path + meg + 'InvOp_SD_EMEG-inv.fif'
inv_fname_LD = data_path + meg + 'InvOp_LD_EMEG-inv.fif'
inv_op_SD = read_inverse_operator(inv_fname_SD)
inv_op_LD = read_inverse_operator(inv_fname_LD)
stc_sd = apply_inverse_epochs(epochs_SD,
inv_op_SD,
C.lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
stc_ld = apply_inverse_epochs(epochs_LD,
inv_op_LD,
C.lambda2,
method='MNE',
pick_ori="normal",
return_generator=False)
times = epochs_SD.times
stc_SD_t = []
stc_LD_t = []
src_SD = inv_op_SD['src']
src_LD = inv_op_LD['src']
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices_SD = [src_SD[j]['vertno'] for j in range(2)]
n_signals_tot = 1 + len(vertices_SD[0]) + len(vertices_SD[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
morph_SD = mne.compute_source_morph(src=inv_op_SD['src'],\
subject_from=sub_to, subject_to=C.subject_to,\
spacing=C.spacing_morph, subjects_dir=C.data_path)
morph_LD = mne.compute_source_morph(src= inv_op_LD['src'],\
subject_from=sub_to, subject_to=C.subject_to,\
spacing=C.spacing_morph, subjects_dir=C.data_path)
for n in np.arange(0, len(stc_sd)):
stc_SD_t.append(stc_baseline_correction(stc_sd[n], times))
stc_LD_t.append(stc_baseline_correction(stc_ld[n], times))
for win in np.arange(0, len(C.con_time_window)):
t_min = C.con_time_window[win]
t_max = C.con_time_window[win] + C.con_time_window_len
stc_SD = []
stc_LD = []
for n in np.arange(0, len(stc_sd)):
stc_SD.append(stc_SD_t[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
stc_LD.append(stc_LD_t[n].copy().crop(t_min * 1e-3, t_max * 1e-3))
for k in np.arange(0, 6):
print('Participant : ', i, '/ ROI: ', k)
morphed_labels[k].name = C.rois_labels[k]
seed_ts_sd = mne.extract_label_time_course(stc_SD, morphed_labels[k], \
src_SD, mode='mean_flip',return_generator=False)
seed_ts_ld = mne.extract_label_time_course(stc_LD, morphed_labels[k], \
src_LD, mode='mean_flip',return_generator=False)
for f in np.arange(0, len(C.con_freq_band_psi) - 1):
f_min = C.con_freq_band_psi[f]
f_max = C.con_freq_band_psi[f + 1]
print('Participant : ' , i, '/ ROI: ',k,' win:', win,\
' freq: ',f)
comb_ts_sd = zip(seed_ts_sd, stc_SD)
comb_ts_ld = zip(seed_ts_ld, stc_LD)
psi_SD, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts_sd,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
n_jobs=15)
psi_LD, freqs, times, n_epochs, _ = phase_slope_index(
comb_ts_ld,
mode='fourier',
indices=indices,
sfreq=500,
fmin=f_min,
fmax=f_max,
n_jobs=15)
psi_stc_SD = mne.SourceEstimate(psi_SD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
psi_stc_LD = mne.SourceEstimate(psi_LD, vertices=vertices_SD,\
tmin=t_min*1e-3, tstep=2e-3,subject=sub_to)
stc_total_SD[win][k][f] = morph_SD.apply(psi_stc_SD)
stc_total_LD[win][k][f] = morph_LD.apply(psi_stc_LD)
with open(stc_SD_file_name, "wb") as fp: #Pickling
pickle.dump(stc_total_SD, fp)
with open(stc_LD_file_name, "wb") as fp: #Pickling
pickle.dump(stc_total_LD, fp)
e = time.time()
print(e - s)
# Construct indices to estimate connectivity between the label time course
# and all source space time courses
vertices = [src[i]['vertno'] for i in range(2)]
n_signals_tot = 1 + len(vertices[0]) + len(vertices[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
# Compute the PSI in the frequency range 11Hz-17Hz.
fmin = 11.
fmax = 17.
sfreq = slow_epo_isi.info['sfreq'] # the sampling frequency
psi_slow, freqs, times, n_epochs, _ = phase_slope_index(comb_ts_slow,
mode='multitaper',
indices=indices,
sfreq=sfreq,
fmin=fmin,
fmax=fmax)
psi_fast, freqs, times, n_epochs, _ = phase_slope_index(comb_ts_fast,
mode='multitaper',
indices=indices,
sfreq=sfreq,
fmin=fmin,
fmax=fmax)
# Generate a SourceEstimate with the PSI. This is simple since we used a single
# seed (inspect the indices variable to see how the PSI scores are arranged in
# the output)
psi_slow_stc = mne.SourceEstimate(psi_slow,
vertices=vertices,
